Experiment with Random Difficulty (results based on the bet)
## Nombre de participants à l'expérimentation : 58## Nombre de participants se déclarant comme joueurs : 29## Nombre de femmes se déclarant comme joueuses : 3## Age médian des joueurs : 15Removing Outliers based on BET
## [1] "Outliers BET STANDARD DEVIATION: 3qq8dp8jk, 79pn8m6v8, e58u3sinl, urgv6o806"
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Outliers BET SAVED SHEEPS: "## Empty data.table (0 rows) of 1 col: IDjoueur## Empty data.table (0 rows) of 1 col: IDjoueur## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Outliers BET EXPLOIT DDA: vuq3c2tk6"## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur## Empty data.table (0 rows) of 1 col: IDjoueur## [1] "Total number of outliers: 5"## [1] "Total number of outliers motor task: 2"## [1] "Total number of outliers perceptive task: 1"## [1] "Total number of outliers logical task: 2"Removing Outliers based on CONFIDENCE SCALE
non nécessaire sur ce fichier !!
{r removing.outliers.setup.cs, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS SETUP # #------------------------------------------------------ # # DTM <- DTAll[which(DTAll$nom_du_jeu=="Motrice"),] # DTL <- DTAll[which(DTAll$nom_du_jeu=="Logique2"),] # DTS <- DTAll[which(DTAll$nom_du_jeu=="Sensoriel"),] # # # get.outliers <- function(DTDescMLoc,DTDescSLoc,DTDescLLoc){ # outliersM <- boxplot.stats(DTDescMLoc$var)$out # outliersS <- boxplot.stats(DTDescSLoc$var)$out # outliersL <- boxplot.stats(DTDescLLoc$var)$out # # outliers = data.table(type=character(0),id=character(0)) # setkey(outliers,id) # if(length(outliersM) > 0) # outliers = merge(outliers,data.table(id=DTDescMLoc[var %in% outliersM]$IDjoueur,type="Moteur"),by=c("id","type"),all=TRUE) # if(length(outliersS) > 0) # outliers = merge(outliers,data.table(id=DTDescSLoc[var %in% outliersS]$IDjoueur,type="Sensoriel"),by=c("id","type"),all=TRUE) # if(length(outliersL) > 0) # outliers = merge(outliers,data.table(id=DTDescLLoc[var %in% outliersL]$IDjoueur,type="Logique"),by=c("id","type"),all=TRUE) # # return(outliers) # } # # plot.outliers <- function(DT,title){ # p <- ggplot(DT, # aes(type,var)) + # xlab("Difficulty Type") + # ylab(title) # p <- p + geom_boxplot() + geom_point(shape=1) # print(p) # } #
{r detect.outliers.cs.sd, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS CS STD DEV # #------------------------------------------------------ # DTDescM = DTM[,.(type="Moteur",var=sd(confianceNorm)),by=IDjoueur] # DTDescS = DTS[,.(type="Sensoriel",var=sd(confianceNorm)),by=IDjoueur] # DTDescL = DTL[,.(type="Logique",var=sd(confianceNorm)),by=IDjoueur] # # plot.outliers(rbind(DTDescM,rbind(DTDescL,DTDescS)), "CS Standard Dev"); # # outliers = get.outliers(DTDescM,DTDescS,DTDescL) # print(paste("Outliers CS STANDARD DEVIATION:",toString(outliers$id))) # # DTM[IDjoueur %in% unlist(outliers[type=="Moteur"]$id) ,{plot.diff.curve.cs(.SD,"Outlier CS Sd Motor Task");NULL},by=.(IDjoueur)] # DTS[IDjoueur %in% unlist(outliers[type=="Sensoriel"]$id) ,{plot.diff.curve.cs(.SD,"Outlier CS Sd Sensory Task");NULL},by=.(IDjoueur)] # DTL[IDjoueur %in% unlist(outliers[type=="Logique"]$id) ,{plot.diff.curve.cs(.SD,"Outlier CS Sd Logical Task");NULL},by=.(IDjoueur)] #
{r detect.outliers.win.sum.cs, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS SUM OF WINS # #------------------------------------------------------ # # Difficulty : win sum # # # DTDescM = DTM[,.(type="Moteur",var=sum(gagnant)),by=IDjoueur] # # DTDescS = DTS[,.(type="Sensoriel",var=sum(gagnant)),by=IDjoueur] # # DTDescL = DTL[,.(type="Logique",var=sum(gagnant)),by=IDjoueur] # # # # plot.outliers(rbind(DTDescM,rbind(DTDescL,DTDescS)), "Win Sum"); # # # # outliersLoc = get.outliers(DTDescM,DTDescS,DTDescL) # # outliers = merge(outliers,outliersLoc,by=c("id","type"),all=TRUE) # # print(paste("Outliers :",toString(outliersLoc$id))) # # # # DTM[IDjoueur %in% unlist(outliersLoc[type=="Moteur"]$id) ,{plot.diff.curve(.SD,"Outlier Win Sum Motor Task");NULL},by=.(IDjoueur)] # # DTS[IDjoueur %in% unlist(outliersLoc[type=="Sensoriel"]$id) ,{plot.diff.curve(.SD,"Outlier Win Sum Sensory Task");NULL},by=.(IDjoueur)] # # DTL[IDjoueur %in% unlist(outliersLoc[type=="Logique"]$id) ,{plot.diff.curve(.SD,"Outlier Win Sum Logical Task");NULL},by=.(IDjoueur)] # #
{r detect.outliers.sheeps.saved.cs, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS SAVED SHEEPS # #------------------------------------------------------ # # Difficulty and strategy = saved sheeps # # DTDescM = DTM[,.(type="Moteur",var=max(moutons_sauves)),by=IDjoueur] # # DTDescS = DTS[,.(type="Sensoriel",var=max(moutons_sauves)),by=IDjoueur] # # DTDescL = DTL[,.(type="Logique",var=max(moutons_sauves)),by=IDjoueur] # # # # plot.outliers(rbind(DTDescM,rbind(DTDescL,DTDescS)), "Saved sheeps"); # # # # outliersLoc = get.outliers(DTDescM,DTDescS,DTDescL) # # outliers = merge(outliers,outliersLoc,by=c("id","type"),all=TRUE) # # print(paste("Outliers CS SAVED SHEEPS:",toString(outliersLoc$id))) # # # # DTM[IDjoueur %in% unlist(outliersLoc[type=="Moteur"]$id) ,{plot.diff.curve(.SD,"Outlier Score Motor Task");NULL},by=.(IDjoueur)] # # DTS[IDjoueur %in% unlist(outliersLoc[type=="Sensoriel"]$id) ,{plot.diff.curve(.SD,"Outlier Score Sensory Task");NULL},by=.(IDjoueur)] # # DTL[IDjoueur %in% unlist(outliersLoc[type=="Logique"]$id) ,{plot.diff.curve(.SD,"Outlier Score Logical Task");NULL},by=.(IDjoueur)] # # #
{r detect.outliers.dda.exploit.cs, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS EXPLOIT DDA # #------------------------------------------------------ # # DDA Exploit : Win/Fail delta sum max # # DTDescM = DTM[,.(type="Moteur",var=max(cumulDeltaMise)),by=IDjoueur] # # DTDescS = DTS[,.(type="Sensoriel",var=max(cumulDeltaMise)),by=IDjoueur] # # DTDescL = DTL[,.(type="Logique",var=max(cumulDeltaMise)),by=IDjoueur] # # # # plot.outliers(rbind(DTDescM,rbind(DTDescL,DTDescS)), "Win/Fail delta sum max"); # # # # outliersLoc = get.outliers(DTDescM,DTDescS,DTDescL) # # outliers = merge(outliers,outliersLoc,by=c("id","type"),all=TRUE) # # print(paste("Outliers CS EXPLOIT DDA:",toString(outliersLoc$id))) # # # # DTM[IDjoueur %in% unlist(outliersLoc[type=="Moteur"]$id) ,{plot.diff.curve(.SD,"Outlier Delta Bet Motor Task");NULL},by=.(IDjoueur)] # # DTS[IDjoueur %in% unlist(outliersLoc[type=="Sensoriel"]$id) ,{plot.diff.curve(.SD,"Outlier Delta Bet Sensory Task");NULL},by=.(IDjoueur)] # # DTL[IDjoueur %in% unlist(outliersLoc[type=="Logique"]$id) ,{plot.diff.curve(.SD,"Outlier Delta Bet Logical Task");NULL},by=.(IDjoueur)] #
{r detect.outliers.summary.cs, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS SUMMARY # #------------------------------------------------------ # print(paste("Total number of outliers: ",toString(nrow(unique(outliers,by="id"))))) # print(paste("Total number of outliers motor task: ",toString(nrow(unique(outliers[type=="Moteur"],by="id"))))) # print(paste("Total number of outliers perceptive task: ",toString(nrow(unique(outliers[type=="Logique"],by="id"))))) # print(paste("Total number of outliers logical task: ",toString(nrow(unique(outliers[type=="Sensoriel"],by="id"))))) #
{r remove.outliers.cs, echo=FALSE} # #------------------------------------------------------ # # REMOVING OUTLIERS FROM TABLES # #------------------------------------------------------ # # removing all outliers and creating a new file only for Confidence Scale Outliers # DTM <- DTM[!IDjoueur %in% unlist(outliers[type=="Moteur"]$id)] # DTS <- DTS[!IDjoueur %in% unlist(outliers[type=="Sensoriel"]$id)] # DTL <- DTL[!IDjoueur %in% unlist(outliers[type=="Logique"]$id)] # DTConfidenceScale <- data.table() # DTConfidenceScale <- rbind(DTConfidenceScale,DTL) # DTConfidenceScale <- rbind(DTConfidenceScale,DTM) # DTConfidenceScale <- rbind(DTConfidenceScale,DTS) #
Modeling difficulties
Modeling objective difficulty for motor task
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 1953.7 1975.3 -972.8 1945.7 1620
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1396 -0.7500 0.2888 0.7385 2.8481
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.5631 0.7504
## Number of obs: 1624, groups: IDjoueur, 56
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.0298 0.1873 -5.499 3.83e-08 ***
## difficulty 2.9618 0.2146 13.803 < 2e-16 ***
## timeNorm -0.5280 0.2020 -2.614 0.00895 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.539
## timeNorm -0.571 -0.009## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.##
## Logique2 Motrice Sensoriel
## 0 1624 0
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-1.050110
## 1st Qu.:-0.438217
## Median :-0.118832
## Mean :-0.002364
## 3rd Qu.: 0.296005
## Max. : 1.658440
## [1] "Intercept: -1.03 3.8e-08 ***"
## [1] "Difficulty: 2.96 2.4e-43 ***"
## [1] "Time: -0.528 0.009 **"
## [1] "R2 fixed: 0.16"
## [1] "R2 mixed: 0.29"
## [1] "Cross Val: 0.68"
## [1] "AIC: 2000"## 0% 25% 50% 75% 100%
## -1.6584395 -0.2960052 0.1188317 0.4382172 1.0501105
## 0% 25% 50% 75% 100%
## -1.6584395 -0.2960052 0.1188317 0.4382172 1.0501105
## `geom_smooth()` using method = 'gam'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
Modeling objective difficulty for sensory task
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 1261.1 1282.7 -626.5 1253.1 1620
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.3943 -0.3586 0.1131 0.3536 6.6338
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.7241 0.8509
## Number of obs: 1624, groups: IDjoueur, 56
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.3288 0.2583 -12.885 <2e-16 ***
## difficulty 8.2778 0.4068 20.346 <2e-16 ***
## timeNorm -0.2933 0.2674 -1.097 0.273
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.650
## timeNorm -0.519 -0.046## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : Model failed to converge with max|grad| = 2.21089 (tol =
## 0.001, component 1)## The result is correct only if all data used by the model has not changed since model was fitted.## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : Model failed to converge with max|grad| = 2.21089 (tol =
## 0.001, component 1)## The result is correct only if all data used by the model has not changed since model was fitted.##
## Logique2 Motrice Sensoriel
## 0 0 1624
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-1.6765404
## 1st Qu.:-0.4435738
## Median : 0.0778425
## Mean :-0.0007671
## 3rd Qu.: 0.4353921
## Max. : 1.5192471
## [1] "Intercept: -3.33 5.5e-38 ***"
## [1] "Difficulty: 8.28 5e-92 ***"
## [1] "Time: -0.293 0.27 :("
## [1] "R2 fixed: 0.34"
## [1] "R2 mixed: 0.44"
## [1] "Cross Val: 0.82"
## [1] "AIC: 1300"## 0% 25% 50% 75% 100%
## -1.51924712 -0.43539206 -0.07784249 0.44357377 1.67654045
## 0% 25% 50% 75% 100%
## -1.51924712 -0.43539206 -0.07784249 0.44357377 1.67654045
## `geom_smooth()` using method = 'gam'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
Modeling objective difficulty for logical task
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 1552.8 1574.4 -772.4 1544.8 1649
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.0811 -0.4934 -0.1180 0.4990 5.2065
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 1.53 1.237
## Number of obs: 1653, groups: IDjoueur, 57
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.7716 0.2500 -7.087 1.37e-12 ***
## difficulty 5.7158 0.3070 18.615 < 2e-16 ***
## timeNorm -2.1395 0.2486 -8.608 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.487
## timeNorm -0.373 -0.253## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.##
## Logique2 Motrice Sensoriel
## 1653 0 0
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-1.8176657
## 1st Qu.:-0.7404031
## Median :-0.2056618
## Mean :-0.0000472
## 3rd Qu.: 0.7132065
## Max. : 3.1485721
## [1] "Intercept: -1.77 1.4e-12 ***"
## [1] "Difficulty: 5.72 2.4e-77 ***"
## [1] "Time: -2.14 7.5e-18 ***"
## [1] "R2 fixed: 0.39"
## [1] "R2 mixed: 0.58"
## [1] "Cross Val: 0.79"
## [1] "AIC: 1600"## 0% 25% 50% 75% 100%
## -3.1485721 -0.7132065 0.2056618 0.7404031 1.8176657
## 0% 25% 50% 75% 100%
## -3.1485721 -0.7132065 0.2056618 0.7404031 1.8176657
## `geom_smooth()` using method = 'gam'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
Influence of Player Profiles
Player profiles
Influence of Player Profiles
Objective level and player profile
Playing video games in general and level for each task
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.3815, p-value = 0.1671
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1442117
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.68759, p-value = 0.4917
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.07199342
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.36057, p-value = 0.7184
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.0374431Playing board games in general and level for each task
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.86453, p-value = 0.3873
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.08913015
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.48979, p-value = 0.6243
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.05061255
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.75722, p-value = 0.4489
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.07770109Self efficacy and level for each task
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 28 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.17852, p-value = 0.8583
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.02429648## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning in cor.test.default(Y, X, method = "kendall"): Removed 28 rows
## containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.4833, p-value = 0.01302
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.3393258
##
## [1] "self.eff.on.level.s 0.34 0.013 *"## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning in cor.test.default(Y, X, method = "kendall"): Removed 28 rows
## containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.69753, p-value = 0.4855
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.09334332Risk aversion and level for each task
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.5679, p-value = 0.1169
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1554335
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.1214, p-value = 0.03389
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2101231
##
## [1] "risk.av.on.level.s 0.21 0.034 *"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.175, p-value = 0.24
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1154221Age and level for each task
## Warning: Removed 1 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.97478, p-value = 0.3297
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.09369113## Warning: Removed 1 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.2162, p-value = 0.02668
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2137687
##
## [1] "age.on.level.s 0.21 0.027 *"## Warning: Removed 1 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.1924, p-value = 0.2331
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1137751Sex and level for each task
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -2.1404, p-value = 0.03233
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2377395
##
## [1] "sexe.on.level.m -0.24 0.032 *"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.077873, p-value = 0.9379
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.008649769
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.20601, p-value = 0.8368
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.0226739
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 220, p-value = 0.03213
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.82775747 -0.05457213
## sample estimates:
## difference in location
## -0.4558716
##
## [1] "sexe.on.level.m.2 -0.46 0.032 * mean(A): 0.15 mean(B): -0.31"
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 347, p-value = 0.9453
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.4361429 0.4780691
## sample estimates:
## difference in location
## -0.01100307
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 339, p-value = 0.845
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.7708044 0.5990311
## sample estimates:
## difference in location
## -0.02530146Subjective difficulty and play habits
Playing video game in general and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.17243, p-value = 0.8631
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.01031151Playing board game in general and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -3.4014, p-value = 0.0006705
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.200843
##
## [1] "pbg.on.error -0.2 0.00067 ***"In game level and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.2292, p-value = 0.219
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.06405096
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.79156, p-value = 0.4286
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.07272727
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.50886, p-value = 0.6108
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.04675325
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.56448, p-value = 0.5724
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.05137845Sex and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 3.6947, p-value = 0.0002202
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2347598
##
## [1] "sexe.on.error 0.23 0.00022 ***"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.9474, p-value = 0.05149
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.216304
##
## [1] "sexe.on.error.m 0.22 0.051 ."
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.3448, p-value = 0.01903
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2604541
##
## [1] "sexe.on.error.s 0.26 0.019 *"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.0601, p-value = 0.03939
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.226739
##
## [1] "sexe.on.error.l 0.23 0.039 *"
##
## Wilcoxon rank sum test with continuity correction
##
## data: B and A
## W = 4236, p-value = 0.0002216
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.04230885 0.12402020
## sample estimates:
## difference in location
## 0.08554301
##
## [1] "sexe.on.error.2 0.086 0.00022 *** mean(A): -0.1 mean(B): -0.011"
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 453, p-value = 0.05195
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.0002022052 0.1424522266
## sample estimates:
## difference in location
## 0.07876811
##
## [1] "sexe.on.error.m.2 0.079 0.052 . mean(A): -0.096 mean(B): -0.012"
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 487, p-value = 0.01851
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.01536563 0.15935580
## sample estimates:
## difference in location
## 0.09651898
##
## [1] "sexe.on.error.s.2 0.097 0.019 * mean(A): -0.1 mean(B): -0.0041"
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 471, p-value = 0.03941
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.003843216 0.147684185
## sample estimates:
## difference in location
## 0.08088642
##
## [1] "sexe.on.error.l.2 0.081 0.039 * mean(A): -0.1 mean(B): -0.016"Risk aversion and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.96322, p-value = 0.3354
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.05451705
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.33391, p-value = 0.7384
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.03310158
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.27607, p-value = 0.7825
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.02734478
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.0476, p-value = 0.2948
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1029065Self efficacy and subjective difficulty error
## Warning: Removed 84 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -2.6413, p-value = 0.00826
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2031367
##
## [1] "self.eff.on.error -0.2 0.0083 **"## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 28 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.6463, p-value = 0.09969
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2240675
##
## [1] "self.eff.on.error -0.22 0.1 :("## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning in cor.test.default(Y, X, method = "kendall"): Removed 28 rows
## containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.3311, p-value = 0.1832
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.1818786## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning in cor.test.default(Y, X, method = "kendall"): Removed 28 rows
## containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.527, p-value = 0.1268
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2043462Influence of Objective difficulty on Subjective Difficulty
All tasks
## [1] "all"
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.00840 51 0.73 :(
## 2: 0.09375 0.02700 56 0.12 :(
## 3: 0.15625 -0.01300 57 0.44 :(
## 4: 0.21875 0.04300 58 0.14 :(
## 5: 0.28125 -0.04300 57 0.23 :(
## 6: 0.34375 0.01300 57 0.8 :(
## 7: 0.40625 0.01500 56 0.67 :(
## 8: 0.46875 -0.02500 57 0.5 :(
## 9: 0.53125 -0.00068 55 0.96 :(
## 10: 0.59375 0.00150 58 0.85 :(
## 11: 0.65625 -0.06100 58 0.046 *
## 12: 0.71875 -0.11000 58 3.2e-05 ***
## 13: 0.78125 -0.16000 56 3.7e-08 ***
## 14: 0.84375 -0.19000 56 3.9e-09 ***
## 15: 0.90625 -0.20000 57 4.9e-11 ***
## 16: 0.96875 -0.17000 57 5e-11 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 51 0.73 :(
## 2: 56 0.12 :(
## 3: 57 0.44 :(
## 4: 58 0.14 :(
## 5: 57 0.23 :(
## 6: 57 0.8 :(
## 7: 56 0.67 :(
## 8: 57 0.5 :(
## 9: 55 0.96 :(
## 10: 58 0.85 :(
## 11: 58 0.046 *
## 12: 58 3.2e-05 ***
## 13: 56 3.7e-08 ***
## 14: 56 3.9e-09 ***
## 15: 57 4.9e-11 ***
## 16: 57 5e-11 ***
## [1] 56.5
## [1] 1.71
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.0130 35 0.73 :(
## 2: 0.09375 -0.0045 37 0.99 :(
## 3: 0.15625 -0.0670 43 0.24 :(
## 4: 0.21875 0.0240 41 0.6 :(
## 5: 0.28125 -0.0430 39 0.41 :(
## 6: 0.34375 0.0130 39 0.68 :(
## 7: 0.40625 0.0460 40 0.3 :(
## 8: 0.46875 0.0240 38 0.76 :(
## 9: 0.53125 -0.0220 40 0.91 :(
## 10: 0.59375 -0.0100 41 0.9 :(
## 11: 0.65625 -0.0630 35 0.093 .
## 12: 0.71875 -0.1500 38 0.00024 ***
## 13: 0.78125 -0.2000 38 0.00015 ***
## 14: 0.84375 -0.2400 26 2.7e-05 ***
## 15: 0.90625 -0.1900 30 1.6e-06 ***
## 16: 0.96875 -0.1500 19 0.00013 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 35 0.73 :(
## 2: 37 0.99 :(
## 3: 43 0.24 :(
## 4: 41 0.6 :(
## 5: 39 0.41 :(
## 6: 39 0.68 :(
## 7: 40 0.3 :(
## 8: 38 0.76 :(
## 9: 40 0.91 :(
## 10: 41 0.9 :(
## 11: 35 0.093 .
## 12: 38 0.00024 ***
## 13: 38 0.00015 ***
## 14: 26 2.7e-05 ***
## 15: 30 1.6e-06 ***
## 16: 19 0.00013 ***
## [1] 36.2
## [1] 6.26
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 -0.0310 30 0.14 :(
## 2: 0.09375 0.0400 33 0.089 .
## 3: 0.15625 0.0580 30 0.51 :(
## 4: 0.21875 -0.0044 38 0.84 :(
## 5: 0.28125 -0.0670 35 0.18 :(
## 6: 0.34375 -0.0530 36 0.48 :(
## 7: 0.40625 -0.0380 36 0.45 :(
## 8: 0.46875 -0.0400 35 0.33 :(
## 9: 0.53125 0.0880 36 0.072 .
## 10: 0.59375 0.0670 35 0.2 :(
## 11: 0.65625 -0.0700 36 0.17 :(
## 12: 0.71875 -0.0760 38 0.1 :(
## 13: 0.78125 -0.0670 39 0.011 *
## 14: 0.84375 -0.1500 37 3.4e-05 ***
## 15: 0.90625 -0.2000 35 2.5e-07 ***
## 16: 0.96875 -0.1900 33 5.5e-07 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 30 0.14 :(
## 2: 33 0.089 .
## 3: 30 0.51 :(
## 4: 38 0.84 :(
## 5: 35 0.18 :(
## 6: 36 0.48 :(
## 7: 36 0.45 :(
## 8: 35 0.33 :(
## 9: 36 0.072 .
## 10: 35 0.2 :(
## 11: 36 0.17 :(
## 12: 38 0.1 :(
## 13: 39 0.011 *
## 14: 37 3.4e-05 ***
## 15: 35 2.5e-07 ***
## 16: 33 5.5e-07 ***
## [1] 35.1
## [1] 2.58
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 -0.0220 9 0.72 :(
## 3: 0.15625 -0.0041 12 1 :(
## 4: 0.21875 0.0055 11 1 :(
## 5: 0.28125 0.0760 11 0.35 :(
## 6: 0.34375 0.1100 9 0.41 :(
## 7: 0.40625 0.0940 12 0.22 :(
## 8: 0.46875 -0.0400 16 0.66 :(
## 9: 0.53125 -0.1400 15 0.2 :(
## 10: 0.59375 -0.1200 13 0.21 :(
## 11: 0.65625 -0.1000 15 0.13 :(
## 12: 0.71875 -0.1500 14 0.033 *
## 13: 0.78125 -0.1900 15 0.0029 **
## 14: 0.84375 -0.1700 18 0.013 *
## 15: 0.90625 -0.1500 18 0.00021 ***
## 16: 0.96875 -0.2200 18 0.00021 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 9 0.72 :(
## 2: 12 1 :(
## 3: 11 1 :(
## 4: 11 0.35 :(
## 5: 9 0.41 :(
## 6: 12 0.22 :(
## 7: 16 0.66 :(
## 8: 15 0.2 :(
## 9: 13 0.21 :(
## 10: 15 0.13 :(
## 11: 14 0.033 *
## 12: 15 0.0029 **
## 13: 18 0.013 *
## 14: 18 0.00021 ***
## 15: 18 0.00021 ***
## [1] 13.7
## [1] 3.06## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
Motor task
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 -0.094 8 0.21 :(
## 3: 0.15625 -0.099 26 0.015 *
## 4: 0.21875 -0.076 40 0.0065 **
## 5: 0.28125 -0.067 45 0.055 .
## 6: 0.34375 -0.058 47 0.21 :(
## 7: 0.40625 -0.013 49 0.8 :(
## 8: 0.46875 0.031 49 0.73 :(
## 9: 0.53125 0.076 51 0.15 :(
## 10: 0.59375 0.025 51 0.55 :(
## 11: 0.65625 -0.013 53 0.45 :(
## 12: 0.71875 -0.052 51 0.079 .
## 13: 0.78125 -0.067 44 0.029 *
## 14: 0.84375 -0.094 27 0.0073 **
## 15: 0.90625 -0.078 14 0.00076 ***
## 16: 0.96875 -0.110 6 0.034 *
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 8 0.21 :(
## 2: 26 0.015 *
## 3: 40 0.0065 **
## 4: 45 0.055 .
## 5: 47 0.21 :(
## 6: 49 0.8 :(
## 7: 49 0.73 :(
## 8: 51 0.15 :(
## 9: 51 0.55 :(
## 10: 53 0.45 :(
## 11: 51 0.079 .
## 12: 44 0.029 *
## 13: 27 0.0073 **
## 14: 14 0.00076 ***
## 15: 6 0.034 *
## [1] 37.4
## [1] 16.7## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 -0.0940 8 0.21 :(
## 3: 0.15625 -0.1200 24 0.005 **
## 4: 0.21875 -0.0760 26 0.031 *
## 5: 0.28125 -0.0670 25 0.12 :(
## 6: 0.34375 0.0130 26 0.8 :(
## 7: 0.40625 0.0320 25 0.67 :(
## 8: 0.46875 0.0880 24 0.14 :(
## 9: 0.53125 0.0760 23 0.21 :(
## 10: 0.59375 0.0970 24 0.038 *
## 11: 0.65625 0.0081 25 0.94 :(
## 12: 0.71875 -0.0470 22 0.078 .
## 13: 0.78125 -0.1000 15 0.26 :(
## 14: 0.84375 NA 0 NA
## 15: 0.90625 NA 0 NA
## 16: 0.96875 NA 0 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 8 0.21 :(
## 2: 24 0.005 **
## 3: 26 0.031 *
## 4: 25 0.12 :(
## 5: 26 0.8 :(
## 6: 25 0.67 :(
## 7: 24 0.14 :(
## 8: 23 0.21 :(
## 9: 24 0.038 *
## 10: 25 0.94 :(
## 11: 22 0.078 .
## 12: 15 0.26 :(
## [1] 22.2
## [1] 5.36## Warning: Removed 4 rows containing missing values (geom_point).## Warning: Removed 4 rows containing missing values (geom_errorbar).
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 0 NA
## 3: 0.15625 0.2000 2 1 :(
## 4: 0.21875 -0.2200 14 0.15 :(
## 5: 0.28125 -0.0990 20 0.38 :(
## 6: 0.34375 -0.1600 20 0.08 .
## 7: 0.40625 -0.0490 22 0.31 :(
## 8: 0.46875 -0.0160 21 0.63 :(
## 9: 0.53125 0.1400 21 0.0048 **
## 10: 0.59375 0.0130 21 0.86 :(
## 11: 0.65625 -0.0130 21 0.94 :(
## 12: 0.71875 0.0430 22 0.43 :(
## 13: 0.78125 -0.0099 21 0.75 :(
## 14: 0.84375 -0.0940 19 0.017 *
## 15: 0.90625 NA 6 NA
## 16: 0.96875 NA 0 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 2 1 :(
## 2: 14 0.15 :(
## 3: 20 0.38 :(
## 4: 20 0.08 .
## 5: 22 0.31 :(
## 6: 21 0.63 :(
## 7: 21 0.0048 **
## 8: 21 0.86 :(
## 9: 21 0.94 :(
## 10: 22 0.43 :(
## 11: 21 0.75 :(
## 12: 19 0.017 *
## [1] 18.7
## [1] 5.66## Warning: Removed 4 rows containing missing values (geom_point).## Warning: Removed 4 rows containing missing values (geom_errorbar).
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 0 NA
## 3: 0.15625 NA 0 NA
## 4: 0.21875 NA 0 NA
## 5: 0.28125 NA 0 NA
## 6: 0.34375 NA 1 NA
## 7: 0.40625 -0.049 2 1 :(
## 8: 0.46875 -0.180 4 0.58 :(
## 9: 0.53125 -0.400 7 0.071 .
## 10: 0.59375 -0.290 6 0.14 :(
## 11: 0.65625 -0.230 7 0.16 :(
## 12: 0.71875 -0.250 7 0.047 *
## 13: 0.78125 -0.180 8 0.023 *
## 14: 0.84375 -0.110 8 0.29 :(
## 15: 0.90625 -0.110 8 0.013 *
## 16: 0.96875 -0.110 6 0.034 *
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 2 1 :(
## 2: 4 0.58 :(
## 3: 7 0.071 .
## 4: 6 0.14 :(
## 5: 7 0.16 :(
## 6: 7 0.047 *
## 7: 8 0.023 *
## 8: 8 0.29 :(
## 9: 8 0.013 *
## 10: 6 0.034 *
## [1] 6.3
## [1] 1.95## Warning: Removed 6 rows containing missing values (geom_point).## Warning: Removed 6 rows containing missing values (geom_errorbar).
Sensory task
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 -0.031 44 0.033 *
## 2: 0.09375 -0.094 53 0.014 *
## 3: 0.15625 -0.071 48 0.046 *
## 4: 0.21875 -0.040 40 0.21 :(
## 5: 0.28125 -0.067 38 0.42 :(
## 6: 0.34375 -0.058 36 0.21 :(
## 7: 0.40625 -0.049 37 0.53 :(
## 8: 0.46875 -0.110 37 0.033 *
## 9: 0.53125 -0.140 30 0.027 *
## 10: 0.59375 -0.170 33 0.029 *
## 11: 0.65625 -0.085 34 0.029 *
## 12: 0.71875 -0.150 34 0.0034 **
## 13: 0.78125 -0.210 38 0.00063 ***
## 14: 0.84375 -0.150 45 8.4e-05 ***
## 15: 0.90625 -0.170 53 1.7e-10 ***
## 16: 0.96875 -0.140 56 6.3e-11 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 44 0.033 *
## 2: 53 0.014 *
## 3: 48 0.046 *
## 4: 40 0.21 :(
## 5: 38 0.42 :(
## 6: 36 0.21 :(
## 7: 37 0.53 :(
## 8: 37 0.033 *
## 9: 30 0.027 *
## 10: 33 0.029 *
## 11: 34 0.029 *
## 12: 34 0.0034 **
## 13: 38 0.00063 ***
## 14: 45 8.4e-05 ***
## 15: 53 1.7e-10 ***
## 16: 56 6.3e-11 ***
## [1] 41
## [1] 7.94
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 -0.0310 19 0.69 :(
## 2: 0.09375 -0.0940 18 0.002 **
## 3: 0.15625 -0.1600 17 0.061 .
## 4: 0.21875 -0.0045 10 0.61 :(
## 5: 0.28125 -0.0670 16 0.51 :(
## 6: 0.34375 -0.2000 12 0.064 .
## 7: 0.40625 -0.1900 12 0.03 *
## 8: 0.46875 -0.2500 15 0.011 *
## 9: 0.53125 -0.3000 11 0.027 *
## 10: 0.59375 -0.2400 12 0.031 *
## 11: 0.65625 -0.1400 12 0.077 .
## 12: 0.71875 -0.3600 11 0.0038 **
## 13: 0.78125 -0.3500 12 0.011 *
## 14: 0.84375 -0.2400 13 0.014 *
## 15: 0.90625 -0.1600 18 0.00019 ***
## 16: 0.96875 -0.1500 19 0.00012 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 19 0.69 :(
## 2: 18 0.002 **
## 3: 17 0.061 .
## 4: 10 0.61 :(
## 5: 16 0.51 :(
## 6: 12 0.064 .
## 7: 12 0.03 *
## 8: 15 0.011 *
## 9: 11 0.027 *
## 10: 12 0.031 *
## 11: 12 0.077 .
## 12: 11 0.0038 **
## 13: 12 0.011 *
## 14: 13 0.014 *
## 15: 18 0.00019 ***
## 16: 19 0.00012 ***
## [1] 14.2
## [1] 3.17
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 -0.031 25 0.0082 **
## 2: 0.09375 -0.094 27 0.31 :(
## 3: 0.15625 -0.120 21 0.058 .
## 4: 0.21875 -0.076 22 0.2 :(
## 5: 0.28125 -0.140 15 0.51 :(
## 6: 0.34375 0.013 19 0.95 :(
## 7: 0.40625 0.022 20 0.72 :(
## 8: 0.46875 -0.110 17 0.25 :(
## 9: 0.53125 -0.100 15 0.44 :(
## 10: 0.59375 -0.150 16 0.31 :(
## 11: 0.65625 -0.160 17 0.17 :(
## 12: 0.71875 -0.076 16 0.15 :(
## 13: 0.78125 -0.067 21 0.11 :(
## 14: 0.84375 -0.130 24 0.0066 **
## 15: 0.90625 -0.190 27 4.7e-06 ***
## 16: 0.96875 -0.140 27 5.5e-06 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 25 0.0082 **
## 2: 27 0.31 :(
## 3: 21 0.058 .
## 4: 22 0.2 :(
## 5: 15 0.51 :(
## 6: 19 0.95 :(
## 7: 20 0.72 :(
## 8: 17 0.25 :(
## 9: 15 0.44 :(
## 10: 16 0.31 :(
## 11: 17 0.17 :(
## 12: 16 0.15 :(
## 13: 21 0.11 :(
## 14: 24 0.0066 **
## 15: 27 4.7e-06 ***
## 16: 27 5.5e-06 ***
## [1] 20.6
## [1] 4.4
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 -0.0220 8 0.94 :(
## 3: 0.15625 0.0220 10 0.61 :(
## 4: 0.21875 0.0260 8 1 :(
## 5: 0.28125 0.0400 7 0.8 :(
## 6: 0.34375 -0.0220 5 0.78 :(
## 7: 0.40625 0.1200 5 0.44 :(
## 8: 0.46875 0.2100 5 0.19 :(
## 9: 0.53125 -0.0250 4 0.62 :(
## 10: 0.59375 -0.0220 5 1 :(
## 11: 0.65625 -0.0130 5 0.78 :(
## 12: 0.71875 0.0063 7 1 :(
## 13: 0.78125 -0.2100 5 0.19 :(
## 14: 0.84375 -0.0580 8 0.29 :(
## 15: 0.90625 -0.1700 8 0.014 *
## 16: 0.96875 -0.1200 10 0.0059 **
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 8 0.94 :(
## 2: 10 0.61 :(
## 3: 8 1 :(
## 4: 7 0.8 :(
## 5: 5 0.78 :(
## 6: 5 0.44 :(
## 7: 5 0.19 :(
## 8: 4 0.62 :(
## 9: 5 1 :(
## 10: 5 0.78 :(
## 11: 7 1 :(
## 12: 5 0.19 :(
## 13: 8 0.29 :(
## 14: 8 0.014 *
## 15: 10 0.0059 **
## [1] 6.67
## [1] 1.95## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
Logical task
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.0130 40 0.95 :(
## 2: 0.09375 0.1200 44 0.015 *
## 3: 0.15625 0.0580 46 0.19 :(
## 4: 0.21875 0.1900 48 0.0019 **
## 5: 0.28125 0.1100 36 0.2 :(
## 6: 0.34375 0.0850 44 0.078 .
## 7: 0.40625 0.0560 43 0.11 :(
## 8: 0.46875 -0.0045 42 0.9 :(
## 9: 0.53125 -0.0310 42 0.45 :(
## 10: 0.59375 -0.0220 46 0.94 :(
## 11: 0.65625 -0.0850 40 0.11 :(
## 12: 0.71875 -0.1500 44 0.016 *
## 13: 0.78125 -0.1400 47 0.00089 ***
## 14: 0.84375 -0.2700 48 1.5e-08 ***
## 15: 0.90625 -0.2400 43 1.1e-08 ***
## 16: 0.96875 -0.3000 29 2.7e-06 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 40 0.95 :(
## 2: 44 0.015 *
## 3: 46 0.19 :(
## 4: 48 0.0019 **
## 5: 36 0.2 :(
## 6: 44 0.078 .
## 7: 43 0.11 :(
## 8: 42 0.9 :(
## 9: 42 0.45 :(
## 10: 46 0.94 :(
## 11: 40 0.11 :(
## 12: 44 0.016 *
## 13: 47 0.00089 ***
## 14: 48 1.5e-08 ***
## 15: 43 1.1e-08 ***
## 16: 29 2.7e-06 ***
## [1] 42.6
## [1] 4.83
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.0130 28 0.75 :(
## 2: 0.09375 0.0490 28 0.33 :(
## 3: 0.15625 0.0580 27 0.69 :(
## 4: 0.21875 0.1900 26 0.0095 **
## 5: 0.28125 -0.0074 18 1 :(
## 6: 0.34375 0.0340 24 0.56 :(
## 7: 0.40625 0.1700 22 0.034 *
## 8: 0.46875 0.1000 21 0.25 :(
## 9: 0.53125 -0.0310 23 0.66 :(
## 10: 0.59375 -0.0760 24 0.15 :(
## 11: 0.65625 -0.1200 17 0.042 *
## 12: 0.71875 -0.1100 21 0.07 .
## 13: 0.78125 -0.1700 23 0.019 *
## 14: 0.84375 -0.2700 21 0.00015 ***
## 15: 0.90625 -0.2600 16 0.00046 ***
## 16: 0.96875 NA 1 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 28 0.75 :(
## 2: 28 0.33 :(
## 3: 27 0.69 :(
## 4: 26 0.0095 **
## 5: 18 1 :(
## 6: 24 0.56 :(
## 7: 22 0.034 *
## 8: 21 0.25 :(
## 9: 23 0.66 :(
## 10: 24 0.15 :(
## 11: 17 0.042 *
## 12: 21 0.07 .
## 13: 23 0.019 *
## 14: 21 0.00015 ***
## 15: 16 0.00046 ***
## [1] 22.6
## [1] 3.76## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 -0.031 12 0.65 :(
## 2: 0.09375 0.330 15 0.011 *
## 3: 0.15625 0.240 16 0.052 .
## 4: 0.21875 0.210 18 0.06 .
## 5: 0.28125 0.150 13 0.14 :(
## 6: 0.34375 0.110 14 0.13 :(
## 7: 0.40625 -0.049 15 0.55 :(
## 8: 0.46875 -0.040 13 0.33 :(
## 9: 0.53125 -0.016 11 1 :(
## 10: 0.59375 0.160 15 0.031 *
## 11: 0.65625 -0.085 14 0.41 :(
## 12: 0.71875 -0.150 17 0.1 :(
## 13: 0.78125 -0.091 17 0.087 .
## 14: 0.84375 -0.240 19 0.00037 ***
## 15: 0.90625 -0.260 18 0.00021 ***
## 16: 0.96875 -0.330 18 0.00021 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 12 0.65 :(
## 2: 15 0.011 *
## 3: 16 0.052 .
## 4: 18 0.06 .
## 5: 13 0.14 :(
## 6: 14 0.13 :(
## 7: 15 0.55 :(
## 8: 13 0.33 :(
## 9: 11 1 :(
## 10: 15 0.031 *
## 11: 14 0.41 :(
## 12: 17 0.1 :(
## 13: 17 0.087 .
## 14: 19 0.00037 ***
## 15: 18 0.00021 ***
## 16: 18 0.00021 ***
## [1] 15.3
## [1] 2.39
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 1 NA
## 3: 0.15625 NA 3 NA
## 4: 0.21875 0.120 4 0.62 :(
## 5: 0.28125 0.150 5 0.27 :(
## 6: 0.34375 0.160 6 0.2 :(
## 7: 0.40625 0.022 6 0.29 :(
## 8: 0.46875 -0.040 8 0.62 :(
## 9: 0.53125 -0.100 8 0.44 :(
## 10: 0.59375 -0.170 7 0.44 :(
## 11: 0.65625 0.058 9 0.72 :(
## 12: 0.71875 -0.076 6 0.84 :(
## 13: 0.78125 -0.190 7 0.15 :(
## 14: 0.84375 -0.340 8 0.042 *
## 15: 0.90625 -0.170 9 0.0091 **
## 16: 0.96875 -0.310 10 0.002 **
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 4 0.62 :(
## 2: 5 0.27 :(
## 3: 6 0.2 :(
## 4: 6 0.29 :(
## 5: 8 0.62 :(
## 6: 8 0.44 :(
## 7: 7 0.44 :(
## 8: 9 0.72 :(
## 9: 6 0.84 :(
## 10: 7 0.15 :(
## 11: 8 0.042 *
## 12: 9 0.0091 **
## 13: 10 0.002 **
## [1] 7.15
## [1] 1.72## Warning: Removed 3 rows containing missing values (geom_point).## Warning: Removed 3 rows containing missing values (geom_errorbar).
Influence of Playtime on Subjective Difficulty Error
For all groups, motor, sensitive and logical
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.85521 -0.20000 0.03999 0.20805 0.69174
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.02757 0.02336 -1.180 0.2381
## timeNorm 0.03482 0.02460 1.416 0.1570
## obj.diff -0.08659 0.03066 -2.824 0.0048 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07429011)
##
## Null deviance: 121.29 on 1623 degrees of freedom
## Residual deviance: 120.42 on 1621 degrees of freedom
## AIC: 391.67
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.78610 -0.11567 0.04559 0.11403 0.81494
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.001581 0.016841 0.094 0.925
## timeNorm 0.009074 0.022514 0.403 0.687
## obj.diff -0.212404 0.017421 -12.192 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06402259)
##
## Null deviance: 113.31 on 1623 degrees of freedom
## Residual deviance: 103.78 on 1621 degrees of freedom
## AIC: 150.11
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.72093 -0.22888 0.01713 0.22812 0.65860
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.16199 0.02321 6.980 4.28e-12 ***
## timeNorm 0.03749 0.02872 1.305 0.192
## obj.diff -0.46231 0.02441 -18.941 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.09806891)
##
## Null deviance: 201.49 on 1652 degrees of freedom
## Residual deviance: 161.81 on 1650 degrees of freedom
## AIC: 857.6
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5089286 0.6008109 -0.07683886 112 0.0057 **
## 2: 4.5 0.4889456 0.5714407 -0.07445527 168 8e-04 ***
## 3: 7.5 0.4863946 0.5416953 -0.04763643 168 0.023 *
## 4: 10.5 0.5008503 0.5401276 -0.03488016 168 0.13 :(
## 5: 13.5 0.4447279 0.5174551 -0.06672273 168 0.0017 **
## 6: 16.5 0.4931973 0.5305272 -0.02102698 168 0.36 :(
## 7: 19.5 0.4736395 0.5315528 -0.04770887 168 0.021 *
## 8: 22.5 0.4455782 0.4897264 -0.03529116 168 0.093 .
## 9: 25.5 0.4464286 0.4805683 -0.02474658 168 0.31 :(
## 10: 28.5 0.4166667 0.4572889 -0.03958163 168 0.083 .
## time error.diff shapes
## 1: 1.5 -0.07683886 24
## 2: 4.5 -0.07445527 24
## 3: 7.5 -0.04763643 24
## 4: 10.5 -0.03488016 16
## 5: 13.5 -0.06672273 24
## 6: 16.5 -0.02102698 16
## 7: 19.5 -0.04770887 24
## 8: 22.5 -0.03529116 16
## 9: 25.5 -0.02474658 16
## 10: 28.5 -0.03958163 16
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4285714 0.5941293 -0.14841500 112 7.1e-09 ***
## 2: 4.5 0.5212585 0.6104788 -0.09151470 168 4.2e-08 ***
## 3: 7.5 0.4379252 0.5299114 -0.09240609 168 1.5e-07 ***
## 4: 10.5 0.4642857 0.5824635 -0.10424800 168 1.8e-11 ***
## 5: 13.5 0.4302721 0.5656294 -0.11814246 168 2.1e-13 ***
## 6: 16.5 0.4064626 0.5333505 -0.11438030 168 4.2e-11 ***
## 7: 19.5 0.4685374 0.5641391 -0.08414982 168 1.9e-08 ***
## 8: 22.5 0.4311224 0.5656705 -0.12484954 168 2.4e-12 ***
## 9: 25.5 0.4923469 0.5874740 -0.09752555 168 7.2e-11 ***
## 10: 28.5 0.4608844 0.5711020 -0.10647805 168 1.2e-10 ***
## time error.diff shapes
## 1: 1.5 -0.14841500 24
## 2: 4.5 -0.09151470 24
## 3: 7.5 -0.09240609 24
## 4: 10.5 -0.10424800 24
## 5: 13.5 -0.11814246 24
## 6: 16.5 -0.11438030 24
## 7: 19.5 -0.08414982 24
## 8: 22.5 -0.12484954 24
## 9: 25.5 -0.09752555 24
## 10: 28.5 -0.10647805 24
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4122807 0.6044431 -0.1751396517 114 1.5e-08 ***
## 2: 4.5 0.5037594 0.6442014 -0.1329386965 171 2.5e-07 ***
## 3: 7.5 0.5037594 0.5633809 -0.0651969309 171 0.0077 **
## 4: 10.5 0.4970760 0.5330653 -0.0461989995 171 0.061 .
## 5: 13.5 0.4761905 0.5198918 -0.0382858444 171 0.15 :(
## 6: 16.5 0.4820384 0.4996879 -0.0306617697 171 0.23 :(
## 7: 19.5 0.4185464 0.4399282 -0.0305348039 171 0.29 :(
## 8: 22.5 0.3918129 0.4071581 -0.0206465199 171 0.47 :(
## 9: 25.5 0.3851295 0.3861396 0.0002090139 171 0.99 :(
## 10: 28.5 0.3792815 0.3521331 -0.0024558459 171 0.93 :(
## time error.diff shapes
## 1: 1.5 -0.1751396517 24
## 2: 4.5 -0.1329386965 24
## 3: 7.5 -0.0651969309 24
## 4: 10.5 -0.0461989995 16
## 5: 13.5 -0.0382858444 16
## 6: 16.5 -0.0306617697 16
## 7: 19.5 -0.0305348039 16
## 8: 22.5 -0.0206465199 16
## 9: 25.5 0.0002090139 16
## 10: 28.5 -0.0024558459 16
For all taks, per group
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group ==
## "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.7392 -0.2163 0.1243 0.1836 0.6877
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.11142 0.04170 2.672 0.0077 **
## timeNorm 0.03093 0.03984 0.776 0.4377
## obj.diff -0.38577 0.04240 -9.099 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.09827124)
##
## Null deviance: 88.057 on 811 degrees of freedom
## Residual deviance: 79.501 on 809 degrees of freedom
## AIC: 425.49
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group ==
## "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.79431 -0.19982 0.05166 0.19690 0.71997
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.06708 0.01956 3.430 0.000616 ***
## timeNorm 0.04788 0.02328 2.057 0.039863 *
## obj.diff -0.28088 0.02172 -12.933 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.08185256)
##
## Null deviance: 175.98 on 1971 degrees of freedom
## Residual deviance: 161.17 on 1969 degrees of freedom
## AIC: 665.7
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group ==
## "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.76793 -0.18358 -0.05094 0.19544 0.77380
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.04974 0.01661 2.995 0.00277 **
## timeNorm 0.02988 0.02152 1.388 0.16523
## obj.diff -0.26359 0.02069 -12.743 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07381598)
##
## Null deviance: 169.06 on 2116 degrees of freedom
## Residual deviance: 156.05 on 2114 degrees of freedom
## AIC: 495.5
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5688776 0.7865986 -0.20015737 56 5.4e-07 ***
## 2: 4.5 0.6105442 0.7834491 -0.13820329 84 1.2e-05 ***
## 3: 7.5 0.6105442 0.7803283 -0.13482737 84 4.7e-07 ***
## 4: 10.5 0.5986395 0.7476758 -0.13141657 84 4.3e-05 ***
## 5: 13.5 0.6122449 0.7699417 -0.11939121 84 3e-05 ***
## 6: 16.5 0.5561224 0.7334575 -0.13531338 84 1.3e-06 ***
## 7: 19.5 0.5578231 0.7066818 -0.11199360 84 0.0011 **
## 8: 22.5 0.5765306 0.7332058 -0.12337951 84 1.2e-05 ***
## 9: 25.5 0.5238095 0.6897234 -0.13639706 84 1.6e-05 ***
## 10: 28.5 0.5969388 0.6715480 -0.06951463 84 0.0089 **
## time error.diff shapes
## 1: 1.5 -0.20015737 24
## 2: 4.5 -0.13820329 24
## 3: 7.5 -0.13482737 24
## 4: 10.5 -0.13141657 24
## 5: 13.5 -0.11939121 24
## 6: 16.5 -0.13531338 24
## 7: 19.5 -0.11199360 24
## 8: 22.5 -0.12337951 24
## 9: 25.5 -0.13639706 24
## 10: 28.5 -0.06951463 24
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4737395 0.6108572 -0.12391990 136 3.4e-06 ***
## 2: 4.5 0.5455182 0.6756446 -0.11461993 204 1.1e-10 ***
## 3: 7.5 0.4880952 0.5392984 -0.05624395 204 0.0026 **
## 4: 10.5 0.5336134 0.5794722 -0.05472763 204 0.0068 **
## 5: 13.5 0.4894958 0.5707053 -0.07912737 204 8.6e-05 ***
## 6: 16.5 0.5147059 0.5552538 -0.04283132 204 0.043 *
## 7: 19.5 0.5063025 0.5632170 -0.05365583 204 0.0052 **
## 8: 22.5 0.4495798 0.5121017 -0.06880287 204 0.0015 **
## 9: 25.5 0.4922969 0.5224562 -0.04441610 204 0.058 .
## 10: 28.5 0.4656863 0.5059577 -0.05291457 204 0.0066 **
## time error.diff shapes
## 1: 1.5 -0.12391990 24
## 2: 4.5 -0.11461993 24
## 3: 7.5 -0.05624395 24
## 4: 10.5 -0.05472763 24
## 5: 13.5 -0.07912737 24
## 6: 16.5 -0.04283132 24
## 7: 19.5 -0.05365583 24
## 8: 22.5 -0.06880287 24
## 9: 25.5 -0.04441610 16
## 10: 28.5 -0.05291457 24
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3816047 0.5179021 -0.11947643 146 9.7e-07 ***
## 2: 4.5 0.4259622 0.4798159 -0.05945139 219 0.0048 **
## 3: 7.5 0.4135682 0.4602904 -0.04895745 219 0.014 *
## 4: 10.5 0.4018265 0.4508328 -0.05577496 219 0.0029 **
## 5: 13.5 0.3522505 0.4098663 -0.06186834 219 0.00068 ***
## 6: 16.5 0.3737769 0.4077438 -0.03978670 219 0.027 *
## 7: 19.5 0.3639922 0.3883398 -0.03472712 219 0.041 *
## 8: 22.5 0.3385519 0.3692817 -0.03631424 219 0.049 *
## 9: 25.5 0.3613829 0.3696034 -0.01725788 219 0.38 :(
## 10: 28.5 0.3065884 0.3349728 -0.04447969 219 0.018 *
## time error.diff shapes
## 1: 1.5 -0.11947643 24
## 2: 4.5 -0.05945139 24
## 3: 7.5 -0.04895745 24
## 4: 10.5 -0.05577496 24
## 5: 13.5 -0.06186834 24
## 6: 16.5 -0.03978670 24
## 7: 19.5 -0.03472712 24
## 8: 22.5 -0.03631424 24
## 9: 25.5 -0.01725788 16
## 10: 28.5 -0.04447969 24
Per group, motor task
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group ==
## "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.77782 -0.16141 0.07773 0.18154 0.65784
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.462332 0.119085 -3.882 0.000135 ***
## timeNorm 0.004994 0.071725 0.070 0.944555
## obj.diff 0.309216 0.135862 2.276 0.023773 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.09109535)
##
## Null deviance: 21.338 on 231 degrees of freedom
## Residual deviance: 20.861 on 229 degrees of freedom
## AIC: 107.53
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.6339286 0.8544830 -0.1813070 16 0.0013 **
## 2: 4.5 0.5773810 0.7995145 -0.1900501 24 0.00057 ***
## 3: 7.5 0.5714286 0.7551085 -0.1583598 24 0.0043 **
## 4: 10.5 0.5892857 0.7836615 -0.1770491 24 0.0011 **
## 5: 13.5 0.6071429 0.8240112 -0.1620422 24 0.0018 **
## 6: 16.5 0.4821429 0.7818411 -0.2673553 24 0.00028 ***
## 7: 19.5 0.5000000 0.7263256 -0.2097781 24 0.0096 **
## 8: 22.5 0.6130952 0.7654436 -0.1099361 24 0.11 :(
## 9: 25.5 0.5119048 0.7908307 -0.2703569 24 0.00018 ***
## 10: 28.5 0.5476190 0.7394768 -0.1501698 24 0.0087 **
## time error.diff shapes
## 1: 1.5 -0.1813070 24
## 2: 4.5 -0.1900501 24
## 3: 7.5 -0.1583598 24
## 4: 10.5 -0.1770491 24
## 5: 13.5 -0.1620422 24
## 6: 16.5 -0.2673553 24
## 7: 19.5 -0.2097781 24
## 8: 22.5 -0.1099361 16
## 9: 25.5 -0.2703569 24
## 10: 28.5 -0.1501698 24## Warning: Removed 2 rows containing missing values (geom_errorbar).
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group ==
## "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.81780 -0.19138 0.04321 0.17708 0.68822
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.13079 0.04059 -3.222 0.00134 **
## timeNorm 0.07430 0.03785 1.963 0.05008 .
## obj.diff 0.09494 0.05455 1.740 0.08228 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06868557)
##
## Null deviance: 44.015 on 637 degrees of freedom
## Residual deviance: 43.615 on 635 degrees of freedom
## AIC: 106.86
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5227273 0.6251419 -0.087913622 44 0.062 .
## 2: 4.5 0.5432900 0.6224524 -0.067787288 66 0.053 .
## 3: 7.5 0.5064935 0.5482212 -0.033170955 66 0.34 :(
## 4: 10.5 0.5519481 0.5744464 -0.017320378 66 0.7 :(
## 5: 13.5 0.5086580 0.5455378 -0.027725556 66 0.47 :(
## 6: 16.5 0.5519481 0.5560045 0.008925402 66 0.85 :(
## 7: 19.5 0.5519481 0.5704673 -0.010678355 66 0.76 :(
## 8: 22.5 0.4307359 0.5060978 -0.079405018 66 0.035 *
## 9: 25.5 0.4870130 0.4999714 -0.012031106 66 0.76 :(
## 10: 28.5 0.4870130 0.5016324 -0.017813543 66 0.61 :(
## time error.diff shapes
## 1: 1.5 -0.087913622 16
## 2: 4.5 -0.067787288 16
## 3: 7.5 -0.033170955 16
## 4: 10.5 -0.017320378 16
## 5: 13.5 -0.027725556 16
## 6: 16.5 0.008925402 16
## 7: 19.5 -0.010678355 16
## 8: 22.5 -0.079405018 24
## 9: 25.5 -0.012031106 16
## 10: 28.5 -0.017813543 16
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group ==
## "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.7823 -0.1833 0.0022 0.2021 0.7030
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.08025 0.03159 -2.541 0.0113 *
## timeNorm 0.06361 0.03395 1.874 0.0613 .
## obj.diff 0.06975 0.04864 1.434 0.1520
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06414613)
##
## Null deviance: 48.468 on 753 degrees of freedom
## Residual deviance: 48.174 on 751 degrees of freedom
## AIC: 73.823
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4587912 0.5021701 -0.029565591 52 0.5 :(
## 2: 4.5 0.4157509 0.4581003 -0.036201957 78 0.23 :(
## 3: 7.5 0.4432234 0.4705078 -0.025469870 78 0.34 :(
## 4: 10.5 0.4304029 0.4361551 -0.003198821 78 0.91 :(
## 5: 13.5 0.3406593 0.3993679 -0.061812903 78 0.028 *
## 6: 16.5 0.4468864 0.4316421 0.017600071 78 0.45 :(
## 7: 19.5 0.3992674 0.4386951 -0.038308162 78 0.2 :(
## 8: 22.5 0.4065934 0.3910376 0.012148843 78 0.56 :(
## 9: 25.5 0.3919414 0.3686849 0.027863007 78 0.37 :(
## 10: 28.5 0.3168498 0.3329405 -0.021946631 78 0.56 :(
## time error.diff shapes
## 1: 1.5 -0.029565591 16
## 2: 4.5 -0.036201957 16
## 3: 7.5 -0.025469870 16
## 4: 10.5 -0.003198821 16
## 5: 13.5 -0.061812903 24
## 6: 16.5 0.017600071 16
## 7: 19.5 -0.038308162 16
## 8: 22.5 0.012148843 16
## 9: 25.5 0.027863007 16
## 10: 28.5 -0.021946631 16
Per group, sensory task
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group ==
## "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.80757 -0.17473 0.04713 0.10223 0.69034
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.116905 0.047140 2.480 0.0137 *
## timeNorm -0.003195 0.056222 -0.057 0.9547
## obj.diff -0.297981 0.047296 -6.300 1.11e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07124426)
##
## Null deviance: 23.276 on 289 degrees of freedom
## Residual deviance: 20.447 on 287 degrees of freedom
## AIC: 61.893
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5428571 0.6390463 -0.11798394 20 0.097 .
## 2: 4.5 0.6666667 0.6686706 -0.02560888 30 0.79 :(
## 3: 7.5 0.5809524 0.7179520 -0.13199277 30 0.0081 **
## 4: 10.5 0.5952381 0.7022945 -0.10466869 30 0.058 .
## 5: 13.5 0.6047619 0.7355270 -0.11701294 30 0.003 **
## 6: 16.5 0.5571429 0.6316433 -0.11730187 30 0.14 :(
## 7: 19.5 0.6000000 0.6735104 -0.09779902 30 0.17 :(
## 8: 22.5 0.6428571 0.7285240 -0.12471305 30 0.012 *
## 9: 25.5 0.4904762 0.6387517 -0.13496951 30 9.2e-06 ***
## 10: 28.5 0.6095238 0.6238117 -0.05083013 30 0.3 :(
## time error.diff shapes
## 1: 1.5 -0.11798394 16
## 2: 4.5 -0.02560888 16
## 3: 7.5 -0.13199277 24
## 4: 10.5 -0.10466869 16
## 5: 13.5 -0.11701294 24
## 6: 16.5 -0.11730187 16
## 7: 19.5 -0.09779902 16
## 8: 22.5 -0.12471305 24
## 9: 25.5 -0.13496951 24
## 10: 28.5 -0.05083013 16
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group ==
## "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.79242 -0.11474 0.04324 0.10863 0.79907
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.01111 0.02414 -0.460 0.646
## timeNorm 0.04278 0.03211 1.332 0.183
## obj.diff -0.20131 0.02507 -8.031 3.56e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06278399)
##
## Null deviance: 53.141 on 782 degrees of freedom
## Residual deviance: 48.972 on 780 degrees of freedom
## AIC: 59.665
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4576720 0.5856813 -0.13077850 54 0.0017 **
## 2: 4.5 0.5255732 0.6540986 -0.10505796 81 3.3e-07 ***
## 3: 7.5 0.4109347 0.4885319 -0.08564427 81 0.0024 **
## 4: 10.5 0.4726631 0.5978029 -0.10758153 81 2.4e-06 ***
## 5: 13.5 0.4550265 0.5802643 -0.11785181 81 1.9e-06 ***
## 6: 16.5 0.4126984 0.5255582 -0.10656642 81 4.5e-05 ***
## 7: 19.5 0.5044092 0.5760814 -0.07137410 81 0.0011 **
## 8: 22.5 0.4179894 0.5370262 -0.11323691 81 4e-05 ***
## 9: 25.5 0.5291005 0.5877937 -0.08344696 81 2e-04 ***
## 10: 28.5 0.4991182 0.5957763 -0.10218991 81 6.6e-07 ***
## time error.diff shapes
## 1: 1.5 -0.13077850 24
## 2: 4.5 -0.10505796 24
## 3: 7.5 -0.08564427 24
## 4: 10.5 -0.10758153 24
## 5: 13.5 -0.11785181 24
## 6: 16.5 -0.10656642 24
## 7: 19.5 -0.07137410 24
## 8: 22.5 -0.11323691 24
## 9: 25.5 -0.08344696 24
## 10: 28.5 -0.10218991 24
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group ==
## "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.68620 -0.11533 0.00271 0.14644 0.85612
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.00914 0.02642 -0.346 0.730
## timeNorm -0.03358 0.03694 -0.909 0.364
## obj.diff -0.23168 0.02816 -8.228 1.39e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.05848987)
##
## Null deviance: 36.064 on 550 degrees of freedom
## Residual deviance: 32.052 on 548 degrees of freedom
## AIC: 4.4272
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3270677 0.5824937 -0.21975183 38 4.8e-08 ***
## 2: 4.5 0.4385965 0.5178656 -0.07591147 57 0.0016 **
## 3: 7.5 0.4010025 0.4897451 -0.07897515 57 0.00024 ***
## 4: 10.5 0.3834586 0.4975966 -0.09894713 57 1.6e-06 ***
## 5: 13.5 0.3032581 0.4554127 -0.14075030 57 1.1e-06 ***
## 6: 16.5 0.3182957 0.4926908 -0.13608760 57 7.2e-08 ***
## 7: 19.5 0.3483709 0.4896047 -0.10281699 57 2.5e-06 ***
## 8: 22.5 0.3383459 0.5206631 -0.17089406 57 1.1e-07 ***
## 9: 25.5 0.4411028 0.5600315 -0.10217279 57 6.4e-05 ***
## 10: 28.5 0.3283208 0.5082965 -0.14696334 57 4.7e-06 ***
## time error.diff shapes
## 1: 1.5 -0.21975183 24
## 2: 4.5 -0.07591147 24
## 3: 7.5 -0.07897515 24
## 4: 10.5 -0.09894713 24
## 5: 13.5 -0.14075030 24
## 6: 16.5 -0.13608760 24
## 7: 19.5 -0.10281699 24
## 8: 22.5 -0.17089406 24
## 9: 25.5 -0.10217279 24
## 10: 28.5 -0.14696334 24
Per group, logical task
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group ==
## "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.6762 -0.2381 0.1900 0.2315 0.4465
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.31017 0.08980 3.454 0.000635 ***
## timeNorm 0.04949 0.07512 0.659 0.510564
## obj.diff -0.67082 0.08619 -7.783 1.29e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1143324)
##
## Null deviance: 40.995 on 289 degrees of freedom
## Residual deviance: 32.813 on 287 degrees of freedom
## AIC: 199.06
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5428571 0.8798433 -0.30689896 20 1.9e-06 ***
## 2: 4.5 0.5809524 0.8853752 -0.26001959 30 5.6e-05 ***
## 3: 7.5 0.6714286 0.8628805 -0.13732681 30 0.00028 ***
## 4: 10.5 0.6095238 0.7642684 -0.13465409 30 0.026 *
## 5: 13.5 0.6238095 0.7611007 -0.13731106 30 0.12 :(
## 6: 16.5 0.6142857 0.7965648 -0.14899512 30 0.0011 **
## 7: 19.5 0.5619048 0.7241382 -0.10999921 30 0.1 :(
## 8: 22.5 0.4809524 0.7120972 -0.18492560 30 0.00042 ***
## 9: 25.5 0.5666667 0.6598092 -0.08410354 30 0.38 :(
## 10: 28.5 0.6238095 0.6649412 -0.04563626 30 0.37 :(
## time error.diff shapes
## 1: 1.5 -0.30689896 24
## 2: 4.5 -0.26001959 24
## 3: 7.5 -0.13732681 24
## 4: 10.5 -0.13465409 24
## 5: 13.5 -0.13731106 16
## 6: 16.5 -0.14899512 24
## 7: 19.5 -0.10999921 16
## 8: 22.5 -0.18492560 24
## 9: 25.5 -0.08410354 16
## 10: 28.5 -0.04563626 16## Warning: Removed 2 rows containing missing values (geom_errorbar).
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group ==
## "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.6900 -0.3151 0.0800 0.2502 0.5349
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.31221 0.04534 6.885 1.58e-11 ***
## timeNorm -0.02431 0.05181 -0.469 0.639
## obj.diff -0.61092 0.04689 -13.028 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1054177)
##
## Null deviance: 76.912 on 550 degrees of freedom
## Residual deviance: 57.769 on 548 degrees of freedom
## AIC: 329.01
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4398496 0.6300933 -0.177313643 38 0.004 **
## 2: 4.5 0.5764411 0.7678535 -0.202298748 57 1.6e-05 ***
## 3: 7.5 0.5764411 0.6011087 -0.043571927 57 0.33 :(
## 4: 10.5 0.5989975 0.5592427 0.022877420 57 0.67 :(
## 5: 13.5 0.5162907 0.5862626 -0.072128673 57 0.16 :(
## 6: 16.5 0.6165414 0.5965834 0.010731991 57 0.86 :(
## 7: 19.5 0.4561404 0.5365408 -0.091070671 57 0.11 :(
## 8: 22.5 0.5162907 0.4836344 0.029379577 57 0.52 :(
## 9: 25.5 0.4461153 0.4556433 -0.006625731 57 0.93 :(
## 10: 28.5 0.3934837 0.3833288 -0.006660789 57 0.89 :(
## time error.diff shapes
## 1: 1.5 -0.177313643 24
## 2: 4.5 -0.202298748 24
## 3: 7.5 -0.043571927 16
## 4: 10.5 0.022877420 16
## 5: 13.5 -0.072128673 16
## 6: 16.5 0.010731991 16
## 7: 19.5 -0.091070671 16
## 8: 22.5 0.029379577 16
## 9: 25.5 -0.006625731 16
## 10: 28.5 -0.006660789 16
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group ==
## "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.6893 -0.1799 -0.1005 0.2162 0.7143
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.08745 0.02950 2.964 0.00312 **
## timeNorm 0.06180 0.03854 1.604 0.10917
## obj.diff -0.34673 0.03733 -9.289 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.08348187)
##
## Null deviance: 76.797 on 811 degrees of freedom
## Residual deviance: 67.537 on 809 degrees of freedom
## AIC: 293.05
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3469388 0.4886803 -0.125553103 56 0.00094 ***
## 2: 4.5 0.4268707 0.4741611 -0.054867444 84 0.18 :(
## 3: 7.5 0.3945578 0.4308157 -0.046659883 84 0.25 :(
## 4: 10.5 0.3877551 0.4327296 -0.047305474 84 0.084 .
## 5: 13.5 0.3962585 0.3887083 0.010385088 84 0.77 :(
## 6: 16.5 0.3435374 0.3279099 0.001173126 84 0.98 :(
## 7: 19.5 0.3418367 0.2728660 0.039134970 84 0.31 :(
## 8: 22.5 0.2755102 0.2463567 0.021040070 84 0.63 :(
## 9: 25.5 0.2789116 0.2412372 0.017366417 84 0.65 :(
## 10: 28.5 0.2823129 0.2192474 0.018899358 84 0.73 :(
## time error.diff shapes
## 1: 1.5 -0.125553103 24
## 2: 4.5 -0.054867444 16
## 3: 7.5 -0.046659883 16
## 4: 10.5 -0.047305474 16
## 5: 13.5 0.010385088 16
## 6: 16.5 0.001173126 16
## 7: 19.5 0.039134970 16
## 8: 22.5 0.021040070 16
## 9: 25.5 0.017366417 16
## 10: 28.5 0.018899358 16
Link between d(t-1) and error
##
## Call:
## glm(formula = error.subj.diff.mise ~ est.confidence.norm, data = DTM)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.88250 -0.18633 0.02911 0.20258 0.70828
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.03233 0.01319 -2.452 0.0143 *
## est.confidence.norm -0.04676 0.02404 -1.945 0.0520 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07460321)
##
## Null deviance: 121.29 on 1623 degrees of freedom
## Residual deviance: 121.01 on 1622 degrees of freedom
## AIC: 397.5
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.mise ~ est.confidence.norm, data = DTS)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.87489 -0.07127 0.00637 0.08302 0.93991
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.10121 0.01287 -7.861 6.87e-15 ***
## est.confidence.norm -0.02613 0.02168 -1.205 0.228
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06979735)
##
## Null deviance: 113.31 on 1623 degrees of freedom
## Residual deviance: 113.21 on 1622 degrees of freedom
## AIC: 289.36
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.mise ~ est.confidence.norm, data = DTL)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.95075 -0.17767 -0.01378 0.20304 0.88612
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.047897 0.017331 -2.764 0.00578 **
## est.confidence.norm 0.005472 0.029595 0.185 0.85332
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1220407)
##
## Null deviance: 201.49 on 1652 degrees of freedom
## Residual deviance: 201.49 on 1651 degrees of freedom
## AIC: 1218.1
##
## Number of Fisher Scoring iterations: 2## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.mise ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTAll
##
## REML criterion at convergence: 1312.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7933 -0.5698 -0.0297 0.5774 4.4670
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01722 0.1312
## Residual 0.07368 0.2714
## Number of obs: 4901, groups: IDjoueur, 58
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -6.739e-02 1.918e-02 7.800e+01 -3.514 0.000738 ***
## est.confidence.norm -4.748e-03 1.502e-02 4.895e+03 -0.316 0.751937
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.389
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.mise ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTM
##
## REML criterion at convergence: -214
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5365 -0.6725 0.0678 0.6605 2.9209
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.02907 0.1705
## Residual 0.04609 0.2147
## Number of obs: 1624, groups: IDjoueur, 56
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.04080 0.02744 97.10000 -1.487 0.140
## est.confidence.norm -0.02876 0.03048 1490.70000 -0.943 0.346
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.523
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.mise ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTS
##
## REML criterion at convergence: 106.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1422 -0.4882 0.0424 0.4686 4.3994
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01317 0.1148
## Residual 0.05797 0.2408
## Number of obs: 1624, groups: IDjoueur, 56
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.15658 0.02263 130.40000 -6.920 1.84e-10 ***
## est.confidence.norm 0.08218 0.03037 820.80000 2.706 0.00696 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.686
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.mise ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTL
##
## REML criterion at convergence: 839.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5713 -0.6002 -0.0435 0.5873 3.4374
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.03514 0.1874
## Residual 0.08874 0.2979
## Number of obs: 1653, groups: IDjoueur, 57
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.10837 0.03253 119.70000 -3.331 0.00115 **
## est.confidence.norm 0.12438 0.03874 1353.50000 3.211 0.00135 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.606CONFIDENCE SCALE APPROACH
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